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A Network Model Approach to Retrieval in the Semantic Web

机译:语义网中的网络模型检索方法

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摘要

While it is agreed that semantic enrichment of resources would lead to better search results, at present the low coverage of resources on the Web with semantic information presents a major hurdle in realizing the vision of search on the Semantic Web. To address this problem, we investigate how to improve retrieval performance in settings where resources are sparsely annotated with semantic information. Techniques from soft computing are employed to find relevant material that was not originally annotated with the concepts used in a query. We present an associative retrieval model for the Semantic Web and evaluate if and to what extent the use of associative retrieval techniques increases retrieval performance. The evaluation of new retrieval paradigms, such as retrieval based on technology for the Semantic Web, presents an additional challenge since no off-the-shelf test corpora exist. Hence, we give a detailed description of the approach taken to evaluate the information retrieval service we have built.
机译:尽管已经达成共识,资源的语义丰富会带来更好的搜索结果,但目前,语义信息在Web上资源的覆盖率较低,这是实现语义Web上的搜索愿景的主要障碍。为了解决这个问题,我们研究了如何在稀疏地用语义信息注释资源的环境中提高检索性能。采用了软计算技术来查找最初未在查询中使用的概念注释的相关材料。我们为语义网提出了一种关联检索模型,并评估了关联检索技术的使用是否以及在何种程度上提高了检索性能。由于不存在现成的测试语料库,因此对新的检索范式(例如基于语义Web技术的检索)的评估提出了另一个挑战。因此,我们将详细介绍用来评估已建立的信息检索服务的方法。

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